Images acquired by both still and video cameras (or any image acquisition device) can be affected by many factors. This includes both environmental factors, such as weather, and the camera settings. Weather conditions affecting acquired images (both still images and video) can be grouped into static and dynamic weather conditions. Static weather conditions include, cloudiness, fog, haze, and mist. Dynamic weather conditions include many types of precipitation, such as, rain, snow, and hail. Adjusting for both static and dynamic weather conditions is important when taking images outdoors, or in indoor conditions that are similar to outdoor conditions. Indoor conditions may be the existence of smoke from a fire, bright lights part of a studio lighting system, or rain like conditions caused by a sprinkler system.
In addition to environmental factors affecting acquired images, camera settings also affect acquired images. Common camera settings include, the exposure time (or shutter speed), the F-number (or F-stop), and the focus setting (or focal plane). The exposure time is related to how long the film in a traditional camera, or the image sensor in a digital camera, is exposed to incoming light. The F-Number, or F-stop setting, relates to how much light is allowed onto the film or image sensor over the duration of the exposure time. Combined together, the exposure time and the F-number determine how much total light is received by the film or image sensor. The focus setting relates to where the light emitting from the object acquired in the image is focused within the camera.
Both environmental conditions and camera settings affect the performance of computer vision systems. These systems perform better with certain type of images, for example, images containing less noise. Dynamic weather effects can produce a large amount of noise in acquired images reducing the performance of a wide range of computer vision algorithms, such as, feature detection, stereo correspondence, tracking, segmentation, and object recognition.
Various algorithms have been developed for handling static weather effects on image acquisition, however little work has been done on dynamic weather effects.
We have determined that a need exists to reduce the effects of dynamic weather on acquired images without the additional time and expense associated with the use of post processing techniques. We have also determined that a need exists for a rain gauge based on an image acquisition system.